Primary bone tumors are the third most frequent tumors of adolescents and young adults. Osteogenic sarcomas (OS) (35% of all primary bone sarcomas) and Ewing's sarcoma are the most and second most common bone tumors. The most accurate prognostic indicator is percent tumor necrosis post neoadjuvant chemotherapy, as estimated by an experienced pathologist post surgery. A pre-surgical estimate of tumor necrosis and an early or a priori marker of response are necessary to further advance treatment in these diseases. The goals of this proposal are to: 1A) To determine if dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) can reliably predict percent necrosis. 1B) Evaluate the hypothesis that in patients with Ewing's or OS, the a priori or early (18-24 days) DCE-MRI study predicts percent necrosis measured by pathological analysis performed at surgery and disease free survival 2) Compare the predictive value of using "semi-quantitative" analyses vs. quantitative models. We will also compare the effects of measuring the input function directly in order to determine if more complex modeling schemes are more accurate in predicting tumor response. The value of the 2 site exchange model analysis and the effect of relaxing the assumption that equilibrium transcytolemmal water transport remains in the "fast exchange limit" and replacing this concept with a more rigorous two site exchange (2SX) model, and its effect on evaluating the DCE-MRI data relationship to tumor necrosis and response will also be analyzed. This will determine if the method of analysis impacts accuracy of predicting tumor response or percent necrosis. 3) Determine if the DCE-MRI results are superior and independent markers of tumor response compared to current clinical markers. We will compare the prognostic value of the DCE-MRI studies vs. molecular and clinical prognosticators. This project will determine the potential of DCE-MRI to predict tumor necrosis and as an a priori or early marker of tumor response to neoadjuvant therapy. If successful, it will provide a tool to predict failure/response to chemotherapy, resulting in patient specific treatment which will likely enhance outcome.